AI with conflicting objectives?
There are multi-objective optimization problems, where the objective functions may be in conflict with each other, which can potentially have multiple Pareto-optimal solutions. The paper Multi-objective optimization using genetic algorithms: A tutorial (2006) gives a good overview of the multi-objective optimization problem with genetic algorithms, which can be called evolutionary multi-objective optimization (EMO) or multi-objective optimization evolutionary algorithms (MOEAs). 

A common multi-objective genetic algorithm is NSGA (or NSGA-2 and NSGA-3), which stands for Non-dominated Sorting Genetic Algorithm, which is based on the concepts of non-dominated sorting, Pareto front and optimality, niches (sub-populations), and elitism (the best individuals of the current population are carried over to the next generation). 

If you want to play with MOEAs, you may wanna try the Python deap package, which supports, for example, the NSGA-2 algorithm.